Key determinants of the future incidence of cancer across Europe: impact of prevention 2005 - 2009

Objective 3

Estimate the future burden of cancer based on autonomous trends and scenarios of maximal implementation of effective interventions, also taking into account the tremendous variation in determinants and incidence of cancer in Europe.

Work performed:

Scenarios of future cancer incidence were developed and analysed using the PREVENT software in order to estimate the future burden in case of maximal implementation of effective interventions. In order to do this, existing software (PREVENT, developed 20 years ago ) was adapted to the needs and wishes of EUROCADET. In summary, PREVENT underwent the following modifications:

  • A dimension to account for changes in disease occurrence was added (previously not existing)
  • The lag time between exposure and changes in disease risk can now be modelled into different shapes (previously only linear). 
  • Risk factors with a continuous distribution can now be modelled. (Previously only categorical) 
  • A cohort dimension was added to allow assessment of the impact of future prevalence of smoking on smoking-related cancers (previously not existing). 
  • The possibility to model quit and initiation rates of smoking was added
  • Bugs in the software have been fixed.
  • A user-manual has been developed
  • 2-day courses for the use of the Prevent-Eurocadet version have been developed and successfully organised in 5 regional European workshops.

Prevent was originally developed in 1988 to estimate the health benefits of changes in risk factor prevalence for a population. Prevent is based on the epidemiological effect measure "potential impact fraction" (PIF), which derives a proportional change in disease risk from a change in risk factor exposure and the relative risk of that risk factor related to the disease(s) under study. Prevent first calculates the autonomous development of a health outcome of interest (here cancer incidence) by applying the trend impact fraction (TIF) on the baseline incidence rates. TIF and PIF are conceptually the same measure where TIF derives from historical changes in risk factor exposure and PIF from change of risk factor exposure due to an intervention. The proportional changes are calculated towards the predetermined theoretical minimum exposure value, the risk factor exposure that would result in the lowest population risk (e.g. in the case of smoking this would equal a smoking prevalence of 0). After the user specifies a change in risk factor prevalence due to an intervention, Prevent calculates the future incidence rates from the current situation using the TIF for the reference scenario and both PIF and TIF for the intervention scenario. The differences between the scenarios are attributed to the intervention, with the output given in terms of rates and number of expected cases in the future.


Risk factor prevalence data, cancer incidence data and relative risk estimates were combined into basic models for the different risk factors and cancers studied in Eurocadet. Then, the information on effectiveness of interventions was used to make assumptions regarding the effects that interventions might have on a population scale. Using Prevent modelling, several scenarios were made for different European countries and the different risk factors and cancers under study. These scenarios are extensively described in the final report of work package 7, and were largely published in a special issue of the European Journal of Cancer (Eur J Cancer 2010, 46: September 2010

Use of PREVENT after the EUROCADET project
PREVENT has already been used to model health effects of interventions in the post-EUROCADET era, for example:

  • in the DG Sanco project ‘EPIDERM' on epidemiology and primary prevention of skin cancer in Europe (results here), resulting amongst others in a publication on the potential impact of interventions to reduce UV radiation on skin cancer incidence.
  • Soerjomataram et al showed using PREVENT that urban greenways have the potential to increase physical activity levels cost-effectively. 
  • Prevent is in use in Denmark by the Danish Cancer Society, resulting in, among others these publications:
    • Andersson et al. Avoidable cancer cases in the Nordic countries - The impact of overweight and obesity. Eur J Cancer 2017;79:106-118. doi: 10.1016/j.ejca.2017.03.028. 
  • The Colombian National Cancer Institute has approved a project in which PREVENT is used to model population attributable risks and make scenarios very similar to those in Eurocadet. The title of the project is: "Incidencia proyectada de cánceres prevenibles bajo variaciones hipotéticas en la prevalencia de sus factores de riesgo, Colombia, 2016-2050". Cancers under study are breast, cervix, colorectal, lung and liver, and risk factors that will be modeled are alcohol, smoking, overweight and obesity, prevalence of HPV and HepB infection, physical activity, parity and age at first childbirth, dietary fiber consumption and consumption or red and processed meats. Incidence data for this project are modelled based on cancer registry data available here, and prevalence data come from high quality national surveys. The first publication has appeared:
    • De Vries et al. Population attributable fractions for colorectal cancer and red and processed meats in Colombia - a macro-simulation study. Colombia Medica 2017;48(2):64-69


Similar projects

In Australia, a series of papers was produced on attributable risks and population impact fractions:

  • Pandeya et al. Cancers in Australia in 2010 attributable to the consumption of alcohol. Aust N Z J Public Health 2015;39(5):408-13. doi: 10.1111/1753-6405.12456.
  • Nagle et al. Cancers in Australia in 2010 attributable to the consumption of red and processed meat. Aust N Z J Public Health 2015;39(5):429-33. doi: 10.1111/1753-6405.12450.
  • Jordan et al. Cancers in Australia in 2010 attributable to total breastfeeding durations of 12 months or less by parous  women. Aust N Z J Public Health. 2015;39(5):418-21. doi: 10.1111/1753-6405.12457.

Want to make your own Prevent models?
If you want to work with Prevent you need:

  • A previous version of Microsoft ACCESS, it will not work in the recent versions
  • A clear hypothesis of an intervention on exposure to risk factors and health effects to be modelled
  • Age- and sex specific incidence data from your country: the most recent estimated European data are available from EUCAN. These EUCAN data for 2012 are also published by Ferlay et al. You can also use the most recently observed incidence data (usually about 5 years old) provided by the registries to the EUREG database*.
  • Prevalence data for the risk factors under study, from your own bureaus of statistics or you can use those collected by EUROCADET


Publications on scenarios of future incidence of cancer in Europe based on Eurocadet:

  1. Boniol M, Autier P. Prevalence of main cancer lifestyle risk factors in Europe in 2000Eur J Cancer. 2010 Sep;46(14):2534-44
  2. Coebergh JW, Martin-Moreno JM, Soerjomataram I, Renehan AG. The long road towards cancer prevention: 4 steps backward and 8 forwardEur J Cancer. 2010 Sep;46(14):2660-2
  3. de Vries E, Soerjomataram I, Lemmens VE, Coebergh JW, Barendregt JJ, Oenema A, et al. Lifestyle changes and reduction of colon cancer incidence in Europe: A scenario study of physical activity promotion and weight reduction. Eur J Cancer. 2010 Sep;46(14):2605-16
  4. Martin-Moreno JM, Alfonso-Sanchez JL, Harris M, Lopez-Valcarcel BG. The effects of the financial crisis on primary prevention of cancerEur J Cancer. 2010 Sep;46(14):2525-33
  5. Menvielle G, Soerjomataram I, de Vries E, Engholm G, Barendregt JJ, Coebergh JW, et al. Scenarios of future lung cancer incidence by educational level: Modelling study in DenmarkEur J Cancer. 2010 Sep;46(14):2625-32
  6. Renehan AG, Soerjomataram I, Leitzmann MF. Interpreting the epidemiological evidence linking obesity and cancer: A framework for population-attributable risk estimations in EuropeEur J Cancer. 2010 Sep;46(14):2581-92
  7. Renehan AG, Soerjomataram I, Martin-Moreno JM, Coebergh JW. Foreword: Implementing cancer prevention in Europe. Eur J Cancer. 2010 Sep;46(14):2523-4
  8. Soerjomataram I, de Vries E, Engholm G, Paludan-Muller G, Bronnum-Hansen H, Storm HH, et al. Impact of a smoking and alcohol intervention programme on lung and breast cancer incidence in Denmark: An example of dynamic modelling with Prevent. Eur J Cancer. 2010 Sep;46(14):2617-24

* Through the FP7 ERANET project Eurocourse a special EUREG portal was made available in which European cancer registries can continuously upload their most recent incidence data plus active follow-up of potential date of death.